Fostering Integration through a Healthy, Safe Workforce: Eliciting Global Data to Drive Improvement
Bibliographic record
Abstract
Worldwide, at least a quarter of health care workers report anxiety, depression and burnout symptoms.1 The ability to effectively address human factors including fatigue, stress, and poor communication can foster heightened patient safety – reducing the risk of error and/or adverse events across organizations, regions, and systems. An integrated health care system relies on healthy health care workers who can develop deep relationships with patients as well as functional relationships with colleagues and service providers – resulting in integrated knowledge of systems and social determinants of care. 2 In 2022, Health Standards Organization (HSO) collaborated with Canadian organizations working across the care continuum to pilot a comprehensive, integrated workforce assessment instrument. The HSO Workforce Survey™ captures data on patient/resident/client safety, care quality, work environments and well-being. Informed by research, analysis of pre-existing survey instrument data and client consultations, the survey tool enables health care workers to provide meaningful data on key themes including job characteristics, demographics, leadership, work team, well-being and engagement, patient centred care, and patient safety. In particular, survey results enable organizations to identify sources of risk to patients and their workforce, and equally important, learn about the factors that contribute to outstanding performance. Taking approximately 15 minutes to complete, the instrument facilitates data acquisition on performance and psychological health and safety across all health care sectors. Specifically, the survey maps to the National Standard of Canada for Psychological Health and Safety in the Workplace and provides reports at organizational, regional, and system-levels. This collation of data thus facilitates ongoing learning and continuous quality improvement – providing insights to heighten health care worker performance and drive health care integration. In this presentation, HSO will outline the preliminary results from the 17 health care organizations that participated as early adopters of the survey tool from September to December 2022; this includes hospitals, long-term care, home care, mental health facilities and emergency medical services. Collated findings from 10,064 health care workers, representing a 31% response rate, will be shared along with data on demographics, evidence-based decision making, safety incident reporting as well as health and well-being. The presentation will highlight how data-driven insights can measurably improve the wellbeing of health care workers. Linkages between workforce perceptions and quality outcomes will also be discussed. Furthermore, the relevance of compiling and benchmarking global workforce data to foster better, integrated care will be shared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".